Conventional multi-sensor downhole tools include two (or more) optical sensors, each of which is supported by its own signal standardization and fluid characterization algorithms (also called fluid models) for real-time optical fluid analysis. The fluid models are calibrated in a synthetic database and often require frequent updates with expansion of the database.
Frequent upgrades to the sensor design require frequent calibration and maintenance of the fluid models for each sensor. Further, with an increase in the number of downhole tools, calibrating and maintaining the optical sensors becomes costly and time consuming. In addition, since different optical parameters are selected as fluid model inputs for different optical sensors, additional efforts are required for interpreting data obtained from multi-sensor tools to evaluate whether the predictions by the two or more optical sensors regarding the fluid in the flow line are consistent with each other, or to confirm whether any inconsistencies in the predictions are because of using different fluid model inputs. To simplify data interpretation, current practice often chooses predictions from a single sensor as the basis of analysis, requiring further improvement to maximize the underlying value of the other sensor(s).